Accompanied with the trend of “civilian’s Internet”, the users whom received information from the web passively in the past are becoming the information’s active creators gradually, so there are a large number of valuable user-generated-comments on the web. How to mining the concerned points of the potential consumers from the massive data effectively and conveniently
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A main purpose of natural language understanding is that computers can understand human languages and communicate with human beings without trouble. As a shallow semantic analysis aiming at analyzing the semantic structure, semantic role labeling recognizes and classifies the arguments of a given verb. It has a lot of successful achievements in high level tasks
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The objective of shallow semantic parsing is to identify semantic roles for a predicate (either a verb or a noun) in a sentence and, including its agent, patient, time and location etc. As a particular case of shallow semantic parsing, semantic role labeling (SRL) has been widely applied in many natural language processing applications, such
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The objective of shallow semantic parsing is to identify semantic roles for a predicate (either a verb or a noun) in a sentence and, including its agent, patient, time and location etc. As a particular case of shallow semantic parsing, semantic role labeling (SRL) has been widely applied in many natural language processing applications, such
(Read More…)
Semantic role labeling is an implement form of shallow semantic parsing. The task does not perform semantic parsing deeply. It only labels the constituents with semantic roles which have direct relation with the predicate in a sentence. The typical semantic roles include Agent, Patient, Source and Goal and so on. These semantic informtion can give
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Semantic role labeling(SRL) and Chinese sentence similarity computation both are essential tasks and widely used in the Chinese information processing. Recent works on SRL are almost based on supervised method, which need a large labelled corpus. However, this kind of resources are still quite limited for Chinese. In order to deal with this problem, this
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During the last decade, there has been an increasing interest in shallow semantic parsing of natural language. In particular, given a sentence and a predicate, semantic role labeling (SRL) identifies its semantic arguments and classifies their semantic roles. This paper systematically studies feature-based SRL with focus on local modeling and joint modeling. Evaluation on the
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During the last decade, there has been an increasing interest in shallow semantic parsing of natural language. In particular, given a sentence and a predicate, semantic role labeling (SRL) identifies its semantic arguments and classifies their semantic roles. This paper systematically studies feature-based SRL with focus on local modeling and joint modeling. Evaluation on the
(Read More…)
During the last decade, there has been an increasing interest in shallow semantic parsing of natural language. In particular, given a sentence and a predicate, semantic role labeling (SRL) identifies its semantic arguments and classifies their semantic roles. This paper systematically studies feature-based SRL with focus on local modeling and joint modeling. Evaluation on the
(Read More…)
During the last decade, there has been an increasing interest in shallow semantic parsing of natural language. In particular, given a sentence and a predicate, semantic role labeling (SRL) identifies its semantic arguments and classifies their semantic roles. This paper systematically studies feature-based SRL with focus on local modeling and joint modeling. Evaluation on the
(Read More…)
